{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8cbc8b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "airDF = pd.read_csv('./airData/aqi_data_u.csv')\n",
    "cityDF = pd.read_csv('./airData/city.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7ca3758",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>cityname</th>\n",
       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>primary_pollutant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53</td>\n",
       "      <td>33</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time cityname  aqi  pm2_5  pm10  so2  no2   co    o3  \\\n",
       "0  2014-12-31      阿坝州   53     33    55    3   23  1.0  35.0   \n",
       "1  2015-01-31      阿坝州   31     18    29    7   10  0.5  45.0   \n",
       "2  2015-01-30      阿坝州   34     19    30    7   13  0.6  48.0   \n",
       "3  2015-01-29      阿坝州   31     18    31    7   15  0.5  32.0   \n",
       "4  2015-01-28      阿坝州   29     18    29    7   14  0.6  27.0   \n",
       "\n",
       "  primary_pollutant  \n",
       "0              PM10  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cfd9c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>安康</td>\n",
       "      <td>32.704370</td>\n",
       "      <td>109.038045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>阿克苏地区</td>\n",
       "      <td>41.171731</td>\n",
       "      <td>80.269846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>阿里地区</td>\n",
       "      <td>30.404557</td>\n",
       "      <td>81.107669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>阿拉善盟</td>\n",
       "      <td>38.843075</td>\n",
       "      <td>105.695683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city        lat         lng\n",
       "0    阿坝州  31.905763  102.228565\n",
       "1     安康  32.704370  109.038045\n",
       "2  阿克苏地区  41.171731   80.269846\n",
       "3   阿里地区  30.404557   81.107669\n",
       "4   阿拉善盟  38.843075  105.695683"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ada58b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "airDF = airDF.rename(columns={'cityname':'city'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91e6746d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <td>阿坝州</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time city  aqi  pm2_5  pm10  so2  no2   co    o3 primary_pollutant\n",
       "0  2014-12-31  阿坝州   53     33    55    3   23  1.0  35.0              PM10\n",
       "1  2015-01-31  阿坝州   31     18    29    7   10  0.5  45.0               NaN\n",
       "2  2015-01-30  阿坝州   34     19    30    7   13  0.6  48.0               NaN\n",
       "3  2015-01-29  阿坝州   31     18    31    7   15  0.5  32.0               NaN\n",
       "4  2015-01-28  阿坝州   29     18    29    7   14  0.6  27.0               NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "663cd81e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>primary_pollutant</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53</td>\n",
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       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>PM10</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
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       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
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       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time city  aqi  pm2_5  pm10  so2  no2   co    o3 primary_pollutant  \\\n",
       "0  2014-12-31  阿坝州   53     33    55    3   23  1.0  35.0              PM10   \n",
       "1  2015-01-31  阿坝州   31     18    29    7   10  0.5  45.0               NaN   \n",
       "2  2015-01-30  阿坝州   34     19    30    7   13  0.6  48.0               NaN   \n",
       "3  2015-01-29  阿坝州   31     18    31    7   15  0.5  32.0               NaN   \n",
       "4  2015-01-28  阿坝州   29     18    29    7   14  0.6  27.0               NaN   \n",
       "\n",
       "         lat         lng  \n",
       "0  31.905763  102.228565  \n",
       "1  31.905763  102.228565  \n",
       "2  31.905763  102.228565  \n",
       "3  31.905763  102.228565  \n",
       "4  31.905763  102.228565  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF = pd.merge(airDF,cityDF,on='city',how='inner')\n",
    "airCityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f31c66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1887"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF.time.value_counts().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2229bbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "345908"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看03这列数据是否有空值\n",
    "airCityDF['o3'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db727a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for city in airCityDF.city.value_counts().index:\n",
    "    '''\n",
    "    for循环计算每个城市污染物的平均值后替换NaN值\n",
    "    '''\n",
    "    airCityDF.loc[(airCityDF['city'] == city) &(airCityDF['o3'].isnull()),'o3'] = airCityDF[airCityDF['city'] == city]['o3'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b5b4e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF['o3'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9103e429",
   "metadata": {},
   "outputs": [],
   "source": [
    "airCityDF.drop(['primary_pollutant'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a316a178",
   "metadata": {},
   "outputs": [
    {
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       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53</td>\n",
       "      <td>33</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time city  aqi  pm2_5  pm10  so2  no2   co    o3        lat  \\\n",
       "0  2014-12-31  阿坝州   53     33    55    3   23  1.0  35.0  31.905763   \n",
       "1  2015-01-31  阿坝州   31     18    29    7   10  0.5  45.0  31.905763   \n",
       "2  2015-01-30  阿坝州   34     19    30    7   13  0.6  48.0  31.905763   \n",
       "3  2015-01-29  阿坝州   31     18    31    7   15  0.5  32.0  31.905763   \n",
       "4  2015-01-28  阿坝州   29     18    29    7   14  0.6  27.0  31.905763   \n",
       "\n",
       "          lng  \n",
       "0  102.228565  \n",
       "1  102.228565  \n",
       "2  102.228565  \n",
       "3  102.228565  \n",
       "4  102.228565  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98c0c5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time     False\n",
       "city     False\n",
       "aqi      False\n",
       "pm2_5    False\n",
       "pm10     False\n",
       "so2      False\n",
       "no2      False\n",
       "co       False\n",
       "o3       False\n",
       "lat      False\n",
       "lng      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af21d586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time        0\n",
       "city        0\n",
       "aqi      4199\n",
       "pm2_5    6817\n",
       "pm10     4817\n",
       "so2      1631\n",
       "no2      1621\n",
       "co       3493\n",
       "o3       2342\n",
       "lat         0\n",
       "lng         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导⼊numpy模块\n",
    "import numpy as np\n",
    "# ⽤NaN替代0，⽅便填充数据\n",
    "airCityDF[['aqi', 'pm2_5', 'pm10', 'so2', 'no2', 'co', 'o3']] = airCityDF[['aqi', 'pm2_5', 'pm10', 'so2','no2', 'co', 'o3']].replace(0,np.NaN)\n",
    "# 将0值替换后缺失值的数量\n",
    "airCityDF.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0b5807ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "for city in airCityDF.city.value_counts().index:\n",
    "    '''\n",
    "    for循环计算每个城市污染物的平均值后替换NaN值\n",
    "    '''\n",
    "    for pollutant in ['aqi', 'pm2_5', 'pm10', 'so2', 'no2', 'co', 'o3']:\n",
    "        airCityDF.loc[(airCityDF['city'] == city) &(airCityDF[pollutant].isnull()), pollutant] = airCityDF[airCityDF['city']== city][pollutant].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ca1838d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time     0\n",
       "city     0\n",
       "aqi      0\n",
       "pm2_5    0\n",
       "pm10     0\n",
       "so2      0\n",
       "no2      0\n",
       "co       0\n",
       "o3       0\n",
       "lat      0\n",
       "lng      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f17223c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据⽉份创建季节列\n",
    "seasons = {12: 'Winter',\n",
    " 1: 'Winter',\n",
    " 2: 'Winter',\n",
    " 3: 'Spring',\n",
    " 4: 'Spring',\n",
    " 5: 'Spring',\n",
    " 6: 'Summer',\n",
    " 7: 'Summer',\n",
    " 8: 'Summer',\n",
    " 9: 'Autumn',\n",
    " 10: 'Autumn',\n",
    " 11: 'Autumn'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa5a23b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 557424 entries, 0 to 557423\n",
      "Data columns (total 11 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   time    557424 non-null  object \n",
      " 1   city    557424 non-null  object \n",
      " 2   aqi     557424 non-null  float64\n",
      " 3   pm2_5   557424 non-null  float64\n",
      " 4   pm10    557424 non-null  float64\n",
      " 5   so2     557424 non-null  float64\n",
      " 6   no2     557424 non-null  float64\n",
      " 7   co      557424 non-null  float64\n",
      " 8   o3      557424 non-null  float64\n",
      " 9   lat     557424 non-null  float64\n",
      " 10  lng     557424 non-null  float64\n",
      "dtypes: float64(9), object(2)\n",
      "memory usage: 51.0+ MB\n"
     ]
    }
   ],
   "source": [
    "airCityDF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa953955",
   "metadata": {},
   "outputs": [],
   "source": [
    "airCityDF['time'] = pd.to_datetime(airCityDF['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "417d6ee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 557424 entries, 0 to 557423\n",
      "Data columns (total 11 columns):\n",
      " #   Column  Non-Null Count   Dtype         \n",
      "---  ------  --------------   -----         \n",
      " 0   time    557424 non-null  datetime64[ns]\n",
      " 1   city    557424 non-null  object        \n",
      " 2   aqi     557424 non-null  float64       \n",
      " 3   pm2_5   557424 non-null  float64       \n",
      " 4   pm10    557424 non-null  float64       \n",
      " 5   so2     557424 non-null  float64       \n",
      " 6   no2     557424 non-null  float64       \n",
      " 7   co      557424 non-null  float64       \n",
      " 8   o3      557424 non-null  float64       \n",
      " 9   lat     557424 non-null  float64       \n",
      " 10  lng     557424 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(9), object(1)\n",
      "memory usage: 51.0+ MB\n"
     ]
    }
   ],
   "source": [
    "airCityDF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3d441c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "airCityDF['season'] = airCityDF['time'].apply(lambda x:seasons[x.month])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "438e717f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>45.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time city   aqi  pm2_5  pm10  so2   no2   co    o3        lat  \\\n",
       "0 2014-12-31  阿坝州  53.0   33.0  55.0  3.0  23.0  1.0  35.0  31.905763   \n",
       "1 2015-01-31  阿坝州  31.0   18.0  29.0  7.0  10.0  0.5  45.0  31.905763   \n",
       "2 2015-01-30  阿坝州  34.0   19.0  30.0  7.0  13.0  0.6  48.0  31.905763   \n",
       "3 2015-01-29  阿坝州  31.0   18.0  31.0  7.0  15.0  0.5  32.0  31.905763   \n",
       "4 2015-01-28  阿坝州  29.0   18.0  29.0  7.0  14.0  0.6  27.0  31.905763   \n",
       "\n",
       "          lng  season  \n",
       "0  102.228565  Winter  \n",
       "1  102.228565  Winter  \n",
       "2  102.228565  Winter  \n",
       "3  102.228565  Winter  \n",
       "4  102.228565  Winter  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "830898c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
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      ],
      "text/plain": [
       "        time city   aqi  pm2_5  pm10  so2   no2   co    o3        lat  \\\n",
       "0 2014-12-31  阿坝州  53.0   33.0  55.0  3.0  23.0  1.0  35.0  31.905763   \n",
       "1 2015-01-31  阿坝州  31.0   18.0  29.0  7.0  10.0  0.5  45.0  31.905763   \n",
       "2 2015-01-30  阿坝州  34.0   19.0  30.0  7.0  13.0  0.6  48.0  31.905763   \n",
       "3 2015-01-29  阿坝州  31.0   18.0  31.0  7.0  15.0  0.5  32.0  31.905763   \n",
       "4 2015-01-28  阿坝州  29.0   18.0  29.0  7.0  14.0  0.6  27.0  31.905763   \n",
       "\n",
       "          lng  season quality  \n",
       "0  102.228565  Winter      良好  \n",
       "1  102.228565  Winter      优级  \n",
       "2  102.228565  Winter      优级  \n",
       "3  102.228565  Winter      优级  \n",
       "4  102.228565  Winter      优级  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_edges = [0, 50, 100, 150, 200, 300, 1210] # 根据AQI的划分等级设置标签\n",
    "bin_names = ['优级', '良好', '轻度污染', '中度污染', '重度污染', '重污染']\n",
    "# cut⽅法会根据bin_edges⾃动打上bin_names\n",
    "airCityDF['quality'] = pd.cut(airCityDF['aqi'], bin_edges,labels=bin_names)\n",
    "airCityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e622b2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_province={'阿坝州': '四川省', '安康': '陕西省', '阿克苏地区': '新疆维吾尔自治区', '阿里地区': '西藏区', '阿拉善盟': '内蒙古自治区', '安庆': '安徽省', '安顺': '贵州省', '鞍山': '辽宁省', '克孜勒苏州': '新疆维吾尔自治区', '安阳': '河南省', '蚌埠': '安徽省', '白城': '吉林省', '北海': '广西壮族自治区', '宝鸡': '陕西省', '毕节': '贵州省', '白山': '吉林省', '百色': '广西壮族自治区', '保山': '云南省', '包头': '内蒙古自治区', '本溪': '辽宁省', '巴彦淖尔': '内蒙古自治区', '白银': '甘肃省', '巴中': '四川省', '滨州': '山东省', '亳州': '安徽省', '昌都': '西藏区', '常德': '湖南省', '赤峰': '内蒙古自治区', '昌吉州': '新疆维吾尔自治区', '五家渠': '新疆维吾尔自治区', '楚雄州': '云南省', '朝阳': '辽宁省', '长治': '山西省', '潮州': '广东省', '郴州': '湖南省', '池州': '安徽省', '崇左': '广西壮族自治区', '滁州': '安徽省', '丹东': '辽宁省', '德宏州': '云南省', '大理州': '云南省', '大庆': '黑龙江', '大同': '山西省', '定西': '甘肃省', '大兴安岭地区': '黑龙江', '德阳': '四川省', '东营': '山东省', '黔南州': '贵州省', '达州': '四川省', '德州': '山东省', '鄂尔多斯': '内蒙古自治区', '恩施州': '湖北省', '鄂州': '湖北省', '防城港': '广西壮族自治区', '抚顺': '辽宁省', '阜新': '辽宁省', '阜阳': '安徽省', '抚州': '江西省', '广安': '四川省', '贵港': '广西壮族自治区', '桂林': '广西壮族自治区', '果洛州': '青海省', '甘南州': '甘肃省', '广元': '四川省', '甘孜州': '四川省', '赣州': '江西省', '海北州': '青海省', '鹤壁': '河南省', '淮北': '安徽省', '河池': '广西壮族自治区', '海东地区': '青海省', '鹤岗': '黑龙江', '黄冈': '湖北省', '黑河': '黑龙江', '红河州': '云南省', '怀化': '湖南省', '呼伦贝尔': '内蒙古自治区', '葫芦岛': '辽宁省', '哈密地区': '新疆维吾尔自治区', '淮南': '安徽省', '黄山': '安徽省', '黄石': '湖北省', '和田地区': '新疆维吾尔自治区', '海西州': '青海省', '河源': '广东省', '衡阳': '湖南省', '汉中': '陕西省', '菏泽': '山东省', '贺州': '广西壮族自治区', '吉安': '江西省', '金昌': '甘肃省', '晋城': '山西省', '景德镇': '江西省', '西双版纳州': '云南省', '九江': '江西省', '吉林': '吉林省', '荆门': '湖北省', '佳木斯': '黑龙江', '济宁': '山东省', '酒泉': '甘肃省', '湘西州': '湖南省', '鸡西': '黑龙江', '揭阳': '广东省', '嘉峪关': '甘肃省', '焦作': '河南省', '锦州': '辽宁省', '晋中': '山西省', '荆州': '湖北省', '开封': '河南省', '黔东南州': '贵州省', '克拉玛依': '新疆维吾尔自治区', '喀什地区': '新疆维吾尔自治区', '六安': '安徽省', '来宾': '广西壮族自治区', '聊城': '山东省', '临沧': '云南省', '娄底': '湖南省', '临汾': '山西省', '漯河': '河南省', '丽江': '云南省', '吕梁': '山西省', '陇南': '甘肃省', '六盘水': '贵州省', '乐山': '四川省', '凉山州': '四川省', '莱芜': '山东省', '辽阳': '辽宁省', '辽源': '吉林省', '临沂': '山东省', '龙岩': '福建省', '洛阳': '河南省', '林芝': '西藏区', '柳州': '广西壮族自治区', '泸州': '四川省', '马鞍山': '安徽省', '牡丹江': '黑龙江', '茂名': '广东省', '眉山': '四川省', '绵阳': '四川省', '梅州': '广东省', '南充': '四川省', '宁德': '福建省', '内江': '四川省', '怒江州': '云南省', '南平': '福建省', '那曲地区': '西藏区', '南阳': '河南省', '平顶山': '河南省', '盘锦': '辽宁省', '平凉': '甘肃省', '莆田': '福建省', '萍乡': '江西省', '濮阳': '河南省', '攀枝花': '四川省', '曲靖': '云南省', '齐齐哈尔': '黑龙江', '七台河': '黑龙江', '黔西南州': '贵州省', '清远': '广东省', '庆阳': '甘肃省', '钦州': '广西壮族自治区', '泉州': '福建省', '日喀则': '西藏区', '日照': '山东省', '韶关': '广东省', '绥化': '黑龙江', '石河子': '新疆维吾尔自治区', '商洛': '陕西省', '三明': '福建省', '三门峡': '河南省', '山南': '西藏区', '遂宁': '四川省', '四平': '吉林省', '商丘': '河南省', '上饶': '江西省', '汕头': '广东省', '汕尾': '广东省', '三亚': '海南省', '邵阳': '湖南省', '十堰': '湖北省', '松原': '吉林省', '双鸭山': '黑龙江', '朔州': '山西省', '宿州': '安徽省', '随州': '湖北省', '泰安': '山东省', '塔城地区': '新疆维吾尔自治区', '铜川': '陕西省', '通化': '吉林省', '铁岭': '辽宁省', '通辽': '内蒙古自治区', '铜陵': '安徽省', '吐鲁番地区': '新疆维吾尔自治区', '铜仁地区': '贵州省', '天水': '甘肃省', '潍坊': '山东省', '威海': '山东省', '乌海': '内蒙古自治区', '芜湖': '安徽省', '乌兰察布': '内蒙古自治区', '渭南': '陕西省', '文山州': '云南省', '武威': '甘肃省', '梧州': '广西壮族自治区', '兴安盟': '内蒙古自治区', '许昌': '河南省', '宣城': '安徽省', '孝感': '湖北省', '迪庆州': '云南省', '锡林郭勒盟': '内蒙古自治区', '咸宁': '湖北省', '湘潭': '湖南省', '新乡': '河南省', '咸阳': '陕西省', '新余': '江西省', '信阳': '河南省', '忻州': '山西省', '雅安': '四川省', '延安': '陕西省', '延边州': '吉林省', '宜宾': '四川省', '宜昌': '湖北省', '宜春': '江西省', '运城': '山西省', '伊春': '黑龙江', '云浮': '广东省', '阳江': '广东省', '营口': '辽宁省', '榆林': '陕西省', '玉林': '广西壮族自治区', '阳泉': '山西省', '玉树州': '青海省', '烟台': '山东省', '鹰潭': '江西省', '玉溪': '云南省', '益阳': '湖南省', '岳阳': '湖南省', '永州': '湖南省', '淄博': '山东省', '自贡': '四川省', '湛江': '广东省', '张家界': '湖南省', '周口': '河南省', '驻马店': '河南省', '昭通': '云南省', '张掖': '甘肃省', '资阳': '四川省', '遵义': '贵州省', '枣庄': '山东省', '漳州': '福建省', '株洲': '湖南省', '深圳': '广东省', '福州': '福建省', '舟山': '浙江省', '青岛': '山东省', '无锡': '江苏省', '湖州': '浙江省', '成都': '四川省', '石家庄': '河北省', '苏州': '新疆维吾尔自治区', '连云港': '江苏省', '徐州': '江苏省', '廊坊': '河北省', '常州': '江苏省', '宿迁': '江苏省', '衡水': '河北省', '兰州': '甘肃省', '邢台': '河北省', '沧州': '河北省', '哈尔滨': '黑龙江', '济南': '山东省', '昆明': '云南省', '扬州': '江苏省', '杭州': '浙江省', '海口': '海南省', '南京': '江苏省', '广州': '广东省', '长沙': '湖南省', '厦门': '福建省', '秦皇岛': '河北省', '张家口': '河北省', '宁波': '浙江省', '南宁': '广西壮族自治区', '盐城': '江苏省', '邯郸': '河北省', '贵阳': '贵州省', '衢州': '浙江省', '承德': '河北省', '南通': '江苏省', '沈阳': '辽宁省', '呼和浩特': '内蒙古自治区', '中山': '广东省', '武汉': '湖北省', '合肥': '安徽省', '长春': '吉林省', '嘉兴': '浙江省', '大连': '辽宁省', '台州': '浙江省', '拉萨': '西藏区', '肇庆': '广东省', '西安': '陕西省', '保定': '河北省', '江门': '广东省', '西宁': '青海省', '乌鲁木齐': '新疆维吾尔自治区', '绍兴': '浙江省', '淮安': '江苏省', '温州': '浙江省', '郑州': '河南省', '惠州': '广东省', '泰州': '新疆维吾尔自治区', '珠海': '广东省', '南昌': '江西省', '唐山': '河北省', '金华': '浙江省', '佛山': '广东省', '东莞': '广东省', '丽水': '浙江省', '太原': '山西省', '镇江': '江苏省', '阿勒泰地区': '新疆维吾尔自治区', '北京': '北京', '博州': '新疆维吾尔自治区', '常熟': '江苏省', '富阳': '浙江省', '固原': '宁夏回族自治区', '海门': '江苏省', '海南州': '青海省', '黄南州': '青海省', ' 即墨': '山东省', '胶南': '山东省', '句容': '江苏省', '金坛': '江苏省', '江阴': '江苏省', '胶州': '山东省', '库尔勒': '新疆维吾尔自治区', '昆山': '江苏省', '临安': '浙江省', '莱西': '山东省', '临夏州': '甘肃省', '溧阳': '江苏省', '莱州': '山东省', '平度': '山东省', '普洱': '云南省', '蓬莱': '山东省', '荣成': '山东省', '乳山': '山东省', '寿光': '山东省', '石嘴山': '宁夏回族自治区', '太仓': '江苏省', '文登': '山东省', '瓦房店': '辽宁省', '吴江': '江苏省', '吴忠': '宁夏回族自治区', '襄阳': '湖北省', '伊犁哈萨克州': '新疆维吾尔自治区', '义乌': '浙江省', '宜兴': '江苏省', '诸暨': '浙江省', '张家港': '江苏省', '章丘': '山东省', '中卫': '宁夏回族自治区', '招远': '山东省', '天津': '天津市', '重庆': '重庆市', '银川': '甘肃省', '上海': '上海市'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c76204b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>city</th>\n",
       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>season</th>\n",
       "      <th>quality</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>良好</td>\n",
       "      <td>四川省</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time city   aqi  pm2_5  pm10  so2   no2   co    o3        lat  \\\n",
       "0 2014-12-31  阿坝州  53.0   33.0  55.0  3.0  23.0  1.0  35.0  31.905763   \n",
       "1 2015-01-31  阿坝州  31.0   18.0  29.0  7.0  10.0  0.5  45.0  31.905763   \n",
       "2 2015-01-30  阿坝州  34.0   19.0  30.0  7.0  13.0  0.6  48.0  31.905763   \n",
       "3 2015-01-29  阿坝州  31.0   18.0  31.0  7.0  15.0  0.5  32.0  31.905763   \n",
       "4 2015-01-28  阿坝州  29.0   18.0  29.0  7.0  14.0  0.6  27.0  31.905763   \n",
       "\n",
       "          lng  season quality province  \n",
       "0  102.228565  Winter      良好      四川省  \n",
       "1  102.228565  Winter      优级      四川省  \n",
       "2  102.228565  Winter      优级      四川省  \n",
       "3  102.228565  Winter      优级      四川省  \n",
       "4  102.228565  Winter      优级      四川省  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map函数可以根据city的值 去dict中取出对应的values\n",
    "airCityDF['province'] = airCityDF['city'].map(city_province)\n",
    "airCityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b4d33242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['海南省', '云南省', '福建省', '贵州省', '西藏区', '广东省', '广西壮族自治区', '黑龙江', '江西省',\n",
       "       '青海省', '内蒙古自治区', '四川省', '湖南省', '浙江省', '吉林省', '甘肃省', '重庆市', '上海市', '辽宁省',\n",
       "       '安徽省', '宁夏回族自治区', '湖北省', '江苏省', '陕西省', '山西省', '山东省', '新疆维吾尔自治区', '天津市',\n",
       "       '河南省', '北京', '河北省'],\n",
       "      dtype='object', name='province')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF.groupby('province').mean().aqi.sort_values().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9ae79900",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 海南省\n",
      "2 云南省\n",
      "3 福建省\n",
      "4 贵州省\n",
      "5 西藏区\n",
      "6 广东省\n",
      "7 广西壮族自治区\n",
      "8 黑龙江\n",
      "9 江西省\n",
      "10 青海省\n",
      "11 内蒙古自治区\n",
      "12 四川省\n",
      "13 湖南省\n",
      "14 浙江省\n",
      "15 吉林省\n",
      "16 甘肃省\n",
      "17 重庆市\n",
      "18 上海市\n",
      "19 辽宁省\n",
      "20 安徽省\n",
      "21 宁夏回族自治区\n",
      "22 湖北省\n",
      "23 江苏省\n",
      "24 陕西省\n",
      "25 山西省\n",
      "26 山东省\n",
      "27 新疆维吾尔自治区\n",
      "28 天津市\n",
      "29 河南省\n",
      "30 北京\n",
      "31 河北省\n"
     ]
    }
   ],
   "source": [
    "for index,v in enumerate(airCityDF.groupby('province').mean().aqi.sort_values().index):\n",
    "    print(index+1,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "064c234c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'海南省': 1, '云南省': 2, '福建省': 3, '贵州省': 4, '西藏区': 5, '广东省': 6, '广西壮族自治区': 7, '黑龙江': 8, '江西省': 9, '青海省': 10, '内蒙古自治区': 11, '四川省': 12, '湖南省': 13, '浙江省': 14, '吉林省': 15, '甘肃省': 16, '重庆市': 17, '上海市': 18, '辽宁省': 19, '安徽省': 20, '宁夏回族自治区': 21, '湖北省': 22, '江苏省': 23, '陕西省': 24, '山西省': 25, '山东省': 26, '新疆维吾尔自治区': 27, '天津市': 28, '河南省': 29, '北京': 30, '河北省': 31}\n"
     ]
    }
   ],
   "source": [
    "provinceRank = airCityDF.groupby('province').mean().aqi.sort_values().index\n",
    "pro_rank_dict = {}\n",
    "for num,province in enumerate(provinceRank):\n",
    "    pro_rank_dict[province] = num + 1\n",
    "print(pro_rank_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "05ebfe65",
   "metadata": {},
   "outputs": [],
   "source": [
    "airCityDF['provinceRank'] = airCityDF.province.map(pro_rank_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7fd445d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>city</th>\n",
       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>season</th>\n",
       "      <th>quality</th>\n",
       "      <th>province</th>\n",
       "      <th>provinceRank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>良好</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time city   aqi  pm2_5  pm10  so2   no2   co    o3        lat  \\\n",
       "0 2014-12-31  阿坝州  53.0   33.0  55.0  3.0  23.0  1.0  35.0  31.905763   \n",
       "1 2015-01-31  阿坝州  31.0   18.0  29.0  7.0  10.0  0.5  45.0  31.905763   \n",
       "2 2015-01-30  阿坝州  34.0   19.0  30.0  7.0  13.0  0.6  48.0  31.905763   \n",
       "3 2015-01-29  阿坝州  31.0   18.0  31.0  7.0  15.0  0.5  32.0  31.905763   \n",
       "4 2015-01-28  阿坝州  29.0   18.0  29.0  7.0  14.0  0.6  27.0  31.905763   \n",
       "\n",
       "          lng  season quality province  provinceRank  \n",
       "0  102.228565  Winter      良好      四川省          12.0  \n",
       "1  102.228565  Winter      优级      四川省          12.0  \n",
       "2  102.228565  Winter      优级      四川省          12.0  \n",
       "3  102.228565  Winter      优级      四川省          12.0  \n",
       "4  102.228565  Winter      优级      四川省          12.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airCityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f30040cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'三亚': 1, '迪庆州': 2, '海口': 3, '丽江': 4, '黔西南州': 5, '怒江州': 6, '阿坝州': 7, '楚雄州': 8, '大理州': 9, '普洱': 10, '大兴安岭地区': 11, '伊春': 12, '甘孜州': 13, '黑河': 14, '林芝': 15, '临沧': 16, '南平': 17, '阿里地区': 18, '阿勒泰地区': 19, '龙岩': 20, '黄山': 21, '西双版纳州': 22, '黔南州': 23, '防城港': 24, '玉溪': 25, '三明': 26, '文山州': 27, '黔东南州': 28, '湛江': 29, '厦门': 30, '塔城地区': 31, '茂名': 32, '锡林郭勒盟': 33, '安顺': 34, '玉树州': 35, '梅州': 36, '汕尾': 37, '深圳': 38, '呼伦贝尔': 39, '保山': 40, '毕节': 41, '日喀则': 42, '昆明': 43, '兴安盟': 44, '宁德': 45, '德宏州': 46, '泉州': 47, '阳江': 48, '凉山州': 49, '曲靖': 50, '铜仁地区': 51, '云浮': 52, '惠州': 53, '钦州': 54, '北海': 55, '河源': 56, '昌都': 57, '鸡西': 58, '莆田': 59, '红河州': 60, '珠海': 61, '梧州': 62, '福州': 63, '汕头': 64, '山南': 65, '贺州': 66, '河池': 67, '佳木斯': 68, '崇左': 69, '双鸭山': 70, '玉林': 71, '贵阳': 72, '巴中': 73, '延边州': 74, '昭通': 75, '漳州': 76, '舟山': 77, '攀枝花': 78, '鹤岗': 79, '六盘水': 80, '百色': 81, '绥化': 82, '景德镇': 83, '陇南': 84, '中山': 85, '齐齐哈尔': 86, '南宁': 87, '贵港': 88, '丽水': 89, '拉萨': 90, '抚州': 91, '遵义': 92, '韶关': 93, '广元': 94, '湘西州': 95, '大庆': 96, '赣州': 97, '揭阳': 98, '来宾': 99, '博州': 100, '潮州': 101, '清远': 102, '新余': 103, '上饶': 104, '克拉玛依': 105, '江门': 106, '鹰潭': 107, '海西州': 108, '郴州': 109, '吉安': 110, '台州': 111, '雅安': 112, '永州': 113, '宜春': 114, '丹东': 115, '恩施州': 116, '海南州': 117, '牡丹江': 118, '温州': 119, '荣成': 120, '怀化': 121, '威海': 122, '白城': 123, '娄底': 124, '肇庆': 125, '通化': 126, '文登': 127, '佛山': 128, '桂林': 129, '商洛': 130, '果洛州': 131, '衢州': 132, '甘南州': 133, '东莞': 134, '赤峰': 135, '南昌': 136, '黄南州': 137, '乳山': 138, '富阳': 139, '广州': 140, '池州': 141, '鄂尔多斯': 142, '遂宁': 143, '乌兰察布': 144, '柳州': 145, '张家界': 146, '通辽': 147, '宁波': 148, '庆阳': 149, '九江': 150, '安康': 151, '宣城': 152, '朝阳': 153, '白山': 154, '益阳': 155, '海北州': 156, '七台河': 157, '衡阳': 158, '张家口': 159, '十堰': 160, '本溪': 161, '广安': 162, '烟台': 163, '固原': 164, '定西': 165, '哈密地区': 166, '大同': 167, '松原': 168, '天水': 169, '大连': 170, '绵阳': 171, '阿拉善盟': 172, '蓬莱': 173, '萍乡': 174, '义乌': 175, '平凉': 176, '邵阳': 177, '临安': 178, '阜新': 179, '资阳': 180, '伊犁哈萨克州': 181, '六安': 182, '咸宁': 183, '巴彦淖尔': 184, '安庆': 185, '辽源': 186, '海门': 187, '岳阳': 188, '常熟': 189, '临夏州': 190, '达州': 191, '张掖': 192, '瓦房店': 193, '榆林': 194, '常德': 195, '铜陵': 196, '南充': 197, '白银': 198, '重庆': 199, '那曲地区': 200, '金华': 201, '昆山': 202, '乐山': 203, '上海': 204, '延安': 205, '内江': 206, '青岛': 207, '太仓': 208, '武威': 209, '金坛': 210, '株洲': 211, '金昌': 212, '承德': 213, '四平': 214, '吴江': 215, '秦皇岛': 216, '黄石': 217, '黄冈': 218, '绍兴': 219, '湘潭': 220, '汉中': 221, '西宁': 222, '溧阳': 223, '盐城': 224, '招远': 225, '嘉峪关': 226, '盘锦': 227, '吕梁': 228, '呼和浩特': 229, '铁岭': 230, '辽阳': 231, '宜宾': 232, '莱州': 233, '随州': 234, '抚顺': 235, '吴忠': 236, '泸州': 237, '南通': 238, '芜湖': 239, '即墨': 240, '吉林': 241, '德阳': 242, '海东地区': 243, '孝感': 244, '句容': 245, '营口': 246, '杭州': 247, '连云港': 248, '嘉兴': 249, '胶州': 250, '淮南': 251, '长沙': 252, '马鞍山': 253, '长春': 254, '葫芦岛': 255, '阜阳': 256, '莱西': 257, '苏州': 258, '荆门': 259, '滁州': 260, '中卫': 261, '鄂州': 262, '眉山': 263, '乌海': 264, '银川': 265, '诸暨': 266, '鞍山': 267, '包头': 268, '蚌埠': 269, '湖州': 270, '酒泉': 271, '张家港': 272, '日照': 273, '平度': 274, '锦州': 275, '哈尔滨': 276, '泰州': 277, '胶南': 278, '扬州': 279, '无锡': 280, '常州': 281, '石嘴山': 282, '宜兴': 283, '镇江': 284, '昌吉州': 285, '信阳': 286, '兰州': 287, '朔州': 288, '南京': 289, '江阴': 290, '忻州': 291, '亳州': 292, '石河子': 293, '淮安': 294, '合肥': 295, '宿迁': 296, '沈阳': 297, '荆州': 298, '铜川': 299, '晋中': 300, '宜昌': 301, '宿州': 302, '自贡': 303, '淮北': 304, '宝鸡': 305, '武汉': 306, '成都': 307, '襄阳': 308, '长治': 309, '晋城': 310, '阳泉': 311, '太原': 312, '商丘': 313, '徐州': 314, '驻马店': 315, '临汾': 316, '运城': 317, '周口': 318, '南阳': 319, '五家渠': 320, '乌鲁木齐': 321, '天津': 322, '许昌': 323, '三门峡': 324, '泰安': 325, '鹤壁': 326, '章丘': 327, '滨州': 328, '漯河': 329, '潍坊': 330, '濮阳': 331, '西安': 332, '渭南': 333, '寿光': 334, '北京': 335, '东营': 336, '沧州': 337, '开封': 338, '济宁': 339, '吐鲁番地区': 340, '临沂': 341, '洛阳': 342, '咸阳': 343, '廊坊': 344, '新乡': 345, '莱芜': 346, '库尔勒': 347, '枣庄': 348, '淄博': 349, '唐山': 350, '济南': 351, '平顶山': 352, '焦作': 353, '郑州': 354, '菏泽': 355, '聊城': 356, '德州': 357, '安阳': 358, '邯郸': 359, '衡水': 360, '克孜勒苏州': 361, '石家庄': 362, '阿克苏地区': 363, '邢台': 364, '保定': 365, '喀什地区': 366, '和田地区': 367}\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>city</th>\n",
       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>season</th>\n",
       "      <th>quality</th>\n",
       "      <th>province</th>\n",
       "      <th>provinceRank</th>\n",
       "      <th>cityRank</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>53.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>良好</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>阿坝州</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.905763</td>\n",
       "      <td>102.228565</td>\n",
       "      <td>Winter</td>\n",
       "      <td>优级</td>\n",
       "      <td>四川省</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time city   aqi  pm2_5  pm10  so2   no2   co    o3        lat  \\\n",
       "0 2014-12-31  阿坝州  53.0   33.0  55.0  3.0  23.0  1.0  35.0  31.905763   \n",
       "1 2015-01-31  阿坝州  31.0   18.0  29.0  7.0  10.0  0.5  45.0  31.905763   \n",
       "2 2015-01-30  阿坝州  34.0   19.0  30.0  7.0  13.0  0.6  48.0  31.905763   \n",
       "3 2015-01-29  阿坝州  31.0   18.0  31.0  7.0  15.0  0.5  32.0  31.905763   \n",
       "4 2015-01-28  阿坝州  29.0   18.0  29.0  7.0  14.0  0.6  27.0  31.905763   \n",
       "\n",
       "          lng  season quality province  provinceRank  cityRank  \n",
       "0  102.228565  Winter      良好      四川省          12.0         7  \n",
       "1  102.228565  Winter      优级      四川省          12.0         7  \n",
       "2  102.228565  Winter      优级      四川省          12.0         7  \n",
       "3  102.228565  Winter      优级      四川省          12.0         7  \n",
       "4  102.228565  Winter      优级      四川省          12.0         7  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cityRank = airCityDF.groupby('city').mean().aqi.sort_values().index\n",
    "city_rank_dict = {}\n",
    "for num,city in enumerate(cityRank):\n",
    "    city_rank_dict[city] = num + 1\n",
    "print(city_rank_dict)\n",
    "# 匹配省名，增加排名列\n",
    "airCityDF['cityRank'] = airCityDF['city'].map(city_rank_dict)\n",
    "airCityDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "783ba22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 广东省的空气质量\n",
    "df_gd = airCityDF[airCityDF['province'] == '广东省']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "edf55b1a",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>53971</th>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>潮州</td>\n",
       "      <td>164.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>80.0</td>\n",
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       "      <td>101</td>\n",
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       "      <td>45.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>101</td>\n",
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       "      <th>53973</th>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>潮州</td>\n",
       "      <td>104.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.0</td>\n",
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       "      <td>116.630076</td>\n",
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       "      <th>53974</th>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>潮州</td>\n",
       "      <td>124.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.661812</td>\n",
       "      <td>116.630076</td>\n",
       "      <td>Winter</td>\n",
       "      <td>轻度污染</td>\n",
       "      <td>广东省</td>\n",
       "      <td>6.0</td>\n",
       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>53975</th>\n",
       "      <td>2014-02-25</td>\n",
       "      <td>潮州</td>\n",
       "      <td>126.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>116.630076</td>\n",
       "      <td>Winter</td>\n",
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       "      <td>广东省</td>\n",
       "      <td>6.0</td>\n",
       "      <td>101</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            time city    aqi  pm2_5   pm10   so2   no2   co     o3        lat  \\\n",
       "53971 2013-12-31   潮州  164.0  125.0  191.0  42.0  80.0  1.7  101.0  23.661812   \n",
       "53972 2014-02-28   潮州   63.0   45.0   64.0  19.0  29.0  1.5   45.0  23.661812   \n",
       "53973 2014-02-27   潮州  104.0   78.0   67.0  17.0  34.0  1.6   32.0  23.661812   \n",
       "53974 2014-02-26   潮州  124.0   95.0   86.0  21.0  40.0  1.8   30.0  23.661812   \n",
       "53975 2014-02-25   潮州  126.0   96.0   54.0  20.0  34.0  1.6   52.0  23.661812   \n",
       "\n",
       "              lng  season quality province  provinceRank  cityRank  \n",
       "53971  116.630076  Winter    中度污染      广东省           6.0       101  \n",
       "53972  116.630076  Winter      良好      广东省           6.0       101  \n",
       "53973  116.630076  Winter    轻度污染      广东省           6.0       101  \n",
       "53974  116.630076  Winter    轻度污染      广东省           6.0       101  \n",
       "53975  116.630076  Winter    轻度污染      广东省           6.0       101  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "72b50d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='o3', ylabel='aqi'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1728 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "df_gd_pollutant = df_gd[df_gd['aqi'] >= 100][['aqi', 'pm2_5', 'pm10', 'so2',\n",
    "'no2', 'co', 'o3']]\n",
    "sns.set_style('whitegrid',{'font.sans-serif':['simhei','Arial']})\n",
    "plt.figure(1,figsize=(16,24))\n",
    "plt.subplot(3,2,1)\n",
    "sns.regplot(x='pm2_5', y='aqi', data=df_gd_pollutant)\n",
    "plt.subplot(3,2,2)\n",
    "sns.regplot(x='pm10', y='aqi', data=df_gd_pollutant)\n",
    "plt.subplot(3,2,3)\n",
    "sns.regplot(x='so2', y='aqi', data=df_gd_pollutant)\n",
    "plt.subplot(3,2,4)\n",
    "sns.regplot(x='no2', y='aqi', data=df_gd_pollutant)\n",
    "plt.subplot(3,2,5)\n",
    "sns.regplot(x='co', y='aqi', data=df_gd_pollutant)\n",
    "plt.subplot(3,2,6)\n",
    "sns.regplot(x='o3', y='aqi', data=df_gd_pollutant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "861cc7f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bd7a8331",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pyecharts'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-d16c438fc9a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharts\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mGeo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mglobals\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mGeoType\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopts\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mkeys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mairCityDF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'city'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maqi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pyecharts'"
     ]
    }
   ],
   "source": [
    "from pyecharts.charts import Geo\n",
    "from pyecharts.globals import GeoType\n",
    "import pyecharts.options as opts\n",
    "\n",
    "keys = airCityDF.groupby('city').aqi.mean().index\n",
    "values = airCityDF.groupby('city').aqi.mean().values\n",
    "\n",
    "# geo = Geo(\"全国主要城市空⽓质量图\", \"data from AQI\")\n",
    "# 创建地图对象，配置长度宽度背景颜色\n",
    "geo = Geo(is_ignore_nonexistent_coord=True,init_opts=opts.InitOpts(bg_color=\"#404a59\", width='800px', height='600px'))\n",
    "# 设置需要绘制的地图\n",
    "geo.add_schema(maptype=\"china\", is_roam=True)\n",
    "\n",
    "# 添加数据\n",
    "geo.add(\"全国主要城市空⽓质量图\",\n",
    "        [list(z) for z in zip(keys, values)],\n",
    "        symbol_size=10,\n",
    "        type_=GeoType.EFFECT_SCATTER\n",
    ")\n",
    "\n",
    "# 对地图进行配置\n",
    "geo.set_global_opts(\n",
    " visualmap_opts=opts.VisualMapOpts(min_=38, max_=194),\n",
    " title_opts=opts.TitleOpts(title=\"全国主要城市空⽓质量图\")\n",
    ")\n",
    "\n",
    "# 设置是否显示图例\n",
    "geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "# 渲染地图，生成html文件\n",
    "geo.render(\"./全国主要城市空⽓质量图.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "12cb9347",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c6a6fbfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = airCityDF.groupby('city').aqi.mean().sort_values().tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ecd05ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1012bacb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f13d0b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cb55312b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a001577e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.font_manager import FontProperties\n",
    "myfont=FontProperties(fname=r'C:\\Windows\\Fonts\\simhei.ttf',size=14)\n",
    "sns.set(font=myfont.get_name())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "59e9bc64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='aqi', ylabel='city'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"aqi\", y=\"city\", data=df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b41f9a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
